- author = "Alex Freitas",
- title = "Data Mining and Knowledge Discovery with Evolutionary Algorithms",
- publisher = "Springer-Verlag",
- year = "2002",
- keywords = "genetic algorithms, genetic programming, data mining, classification, clustering, Artificial Intelligence, Computing Methodologies, Evolutionary Algorithms, Machine Learning",
- ISBN = "0-7923-8048-7",
-
URL = "
https://kar.kent.ac.uk/13669/",
-
URL = "http://www.springer.com/computer/ai/book/978-3-540-43331-6",
-
abstract = "This book integrates two areas of computer science,
namely data mining and evolutionary algorithms. Both
these areas have become increasingly popular in the
last few years, and their integration is currently an
area of active research. In general, data mining
consists of extracting knowledge from data. In this
book we particularly emphasise the importance of
discovering comprehensible and interesting knowledge,
which is potentially useful to the reader for
intelligent decision making. In a nutshell, the
motivation for applying evolutionary algorithms to data
mining is that evolutionary algorithms are robust
search methods which perform a global search in the
space of candidate solutions (rules or another form of
knowledge representation). In contrast, most rule
induction methods perform a local, greedy search in the
space of candidate rules. Intuitively, the global
search of evolutionary algorithms can discover
interesting rules and patterns that would be missed by
the greedy search.
This book presents a comprehensive review of basic concepts on both data mining and evolutionary algorithms and discusses significant advances in the integration of these two areas. It is self-contained, explaining both basic concepts and advanced topics.",
- size = "xiv + 264 pages",
